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Evangelos Talos

dblp:419/4103 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0009-7789-2135ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution › software ecosystems
dependency management
0.912025
PyTrim: A Practical Tool for Reducing Python Dependency Bloat · ASE 2025

Methods — techniques the papers use, named apart from their topics

static analysis · 0.9dynamic analysis · 0.9
YearPublicationVenuePosition
2026 TGIF: the evolution of developer commit times
Ioannis Karyotakis, Evangelos Talos, Diomidis Spinellis
Empir. Softw. Eng.2
2025 PyTrim: A Practical Tool for Reducing Python Dependency Bloat
abstract
Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project requires manual effort and expertise. To tackle this challenge we introduce PYTRIM, an end-to-end system to automate this process. PYTRIM eliminates unused imports and package declarations across a variety of file types, including Python source and configuration files such as requirements.txt and setup.py. PYTRIM’s modular design makes it agnostic to the source of dependency bloat information, enabling integration with any detection tool. Beyond its contribution when it comes to automation, PYTRIM also incorporates a novel dynamic analysis component that improves dependency detection recall. Our evaluation of PYTRIM’s end-to-end effectiveness on a ground-truth dataset of 37 merged pull requests from prior work, shows that PYTRIM achieves 98.3% accuracy in replicating human-made changes. To show its practical impact, we run PYTRIM on 971 open-source packages, identifying and trimming bloated dependencies in 39 of them. For each case, we submit a corresponding pull request, 14 of which have already been accepted and merged. PYTRIM is available as an open-source project, encouraging community contributions and further development.Video demonstration: https://youtu.be/LqTEdOUbJRICode repository: https://github.com/TrimTeam/PyTrim
Konstantinos Karakatsanis, Georgios Alexopoulos, Ioannis Karyotakis, Foivos Timotheos Proestakis, Evangelos Talos, Panagiotis Louridas, Dimitris Mitropoulos
ASE5